Wild Boar and Red Deer Display High Prevalences of Tuberculosis-Like Lesions in Spain

We describe the distribution of tuberculosis-like lesions (TBL) in wild boar (Sus scrofa) and red deer (Cervus elaphus) in Spain. Animals with TBL were confirmed in 84.21% of mixed populations (n = 57) of red deer and wild boar and in 75% of populations of wild boar alone (n = 8) in central and southern Spain (core area). The prevalence of TBL declined towards the periphery of this region. In the core area, the prevalence ranged up to 100% in local populations of wild boar (mean estate prevalence 42.51%) and up to 50% in red deer (mean estate prevalence 13.70%). We carried out exploratory statistical analyses to describe the epidemiology of TBL in both species throughout the core area. Prevalence of TBL increased with age in both species. Wild boar and red deer mean TBL prevalence at the estate level were positively associated, and lesion scores were consistently higher in wild boars than in red deer. The wild boar prevalence of TBL in wild boar did not differ between populations that were or were not cohabiting with red deer. Amongst the wild boars with TBL, 61.19% presented generalized lesions, and the proportion of generalized cases was similar between sex and age classes. In red deer, 57.14% of TBL-positive individuals presented generalized lesions, and the percentage of generalized cases increased with age class, but did not differ between the sexes. These results highlight the potential importance of wild boar and red deer in the maintenance of tuberculosis in south central Spain.

An Extended Kernel for Generalized Multiple-Instance Learning

The multiple-instance learning (MIL) model has been successful in areas such as drug discovery and content-based image-retrieval. Recently, this model was generalized and a corresponding kernel was introduced to learn generalized MIL concepts with a support vector machine. While this kernel enjoyed empirical success, it has limitations in its representation. We extend this kernel by enriching its representation and empirically evaluate our new kernel on data from content-based image retrieval, biological sequence analysis, and drug discovery. We found that our new kernel generalized noticeably better than the old one in content-based image retrieval and biological sequence analysis and was slightly better or even with the old kernel in the other applications, showing that an SVM using this kernel does not overfit despite its richer representation.